scholarly journals SPECTRAL IMAGING AND CLAY DETECTION IN LATGALE LAKES

2019 ◽  
Vol 1 ◽  
pp. 307
Author(s):  
Rasma Tretjakova ◽  
Sergejs Kodors ◽  
Juris Soms
Keyword(s):  
Remote Sensing ◽  
Incomplete Data ◽  
Spectral Imaging ◽  
Cost Effective ◽  
Indirect Methods ◽  
Sediment Samples ◽  
Pilot Experiment ◽  
Pearson Coefficient ◽  
Remote Sensing Methods ◽  
Sentinel 2

The survey of lake sediments is complex, time consuming and costly process with risks to human health. Additionally, manually obtained sediment samples provide incomplete data about a survey region. In turn, remote sensing methods are cost-effective and can provide continuous data about a survey region. Therefore, authors decided to perform a pilot experiment with a remote sensing method in order to detect clay sediments deposited in lakebeds. The evaluated method is the analysis of spectral images of Sentinel-2. Pearson coefficient and C4.5 datamining methods were applied for data analysis. Survey objects are Latgale lakes with and without clay sediments. The pilot experiment showed, that spectral imaging of lake water is not applicable method to detect definitely clay sediments in lakes, however, research results provide ideas about indirect methods, which must be studied in the future.

Nonlinear inversion of subsidence signatures induced by tunnels detected with surface and remote sensing measurements

2019 ◽  
Vol 38 (7) ◽  
pp. 550-553
Author(s):  
Jorge Parra ◽  
Jonathan Parra ◽  
Marius Necsoiu
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Cost Effective ◽  
Forward Modeling ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Gap Parameter ◽  
Remote Sensing Methods ◽  
Good Agreement

The state of the art in predicting tunnel-induced subsidence settlements is based on empirical and analytical methods. Empirical methods are useful when the equations are implemented with host medium properties where tunnels have been excavated. Analytical solutions can predict tunneling-induced ground movements, with the predictions accounting for tunnel radius and depth as well as ground-loss parameters in soft soils. The drawback is that these methods require human intervention, as each model must be adjusted manually by the interpreter until the model signature fits the observed data. It would take tremendous effort to evaluate displacement anomalies detected by remote sensing methods using such forward-modeling methods. Therefore, we present a method based on an inversion algorithm that automatically inverts subsidence signatures for tunnel radius, depth, Poisson's ratio, and the gap parameter. It is an advancement over conventional methods because it does not require a first guess, and it can invert several subsidence signatures in a matter of minutes. The algorithm, coupled with remote sensing-based displacement maps, is a cost-effective solution in operational characterization of displacement anomalies. We demonstrate that observed and predicted subsidence signatures are in good agreement with existing tunnel data in uniform clay and that the inversion parameters correspond to those predicted with forward modeling alone.

Bathymetric mapping by means of remote sensing: methods, accuracy and limitations

2009 ◽  
Vol 33 (1) ◽  
pp. 103-116 ◽  
Author(s):  
Jay Gao
Keyword(s):  
Remote Sensing ◽  
Light Transmission ◽  
Effective Means ◽  
Operating Cost ◽  
Cost Effective ◽  
Sampling Interval ◽  
Influential Factors ◽  
Imaging Method ◽  
Cost Inefficiency ◽  
Remote Sensing Methods

Bathymetry has been traditionally charted via shipboard echo sounding. Alhough able to generate accurate depth measurements at points or along transects, this method is constrained by its high operating cost, inefficiency, and inapplicability to shallow waters. By comparison, remote sensing methods offer more flexible, efficient and cost-effective means of mapping bathymetry over broad areas. Remote sensing of bathymetry falls into two broad categories: non-imaging and imaging methods. The non-imaging method (as typified by LiDAR) is able to produce accurate bathymetric information over clear waters at a depth up to 70 m. However, this method is limited by the coarse bathymetric sampling interval and high cost. The imaging method can be implemented either analytically or empirically, or by a combination of both. Analytical or semi-analytical implementation is based on the manner of light transmission in water. It requires inputs of a number of parameters related to the properties of the atmosphere, water column, and bottom material. Thus, it is rather complex and difficult to use. By comparison, empirical implementation is much simpler and requires the input of fewer parameters. Both implementations can produce fine-detailed bathymetric maps over extensive turbid coastal and inland lake waters quickly, even though concurrent depth samples are essential. The detectable depth is usually limited to 20 m. The accuracy of the retrieved bathymetry varies with water depth, with the accuracy substantially lower at a depth beyond 12 m. Other influential factors include water turbidity and bottom materials, as well as image properties.

scholarly journals The utility of Sentinel-2 Vegetation Indices (VIs) and Sentinel-1 Synthetic Aperture Radar (SAR) for invasive alien species detection and mapping

2019 ◽  
Vol 35 ◽  
pp. 41-61 ◽  
Author(s):  
Perushan Rajah ◽  
John Odindi ◽  
Onisimo Mutanga ◽  
Zolo Kiala
Keyword(s):  
Remote Sensing ◽  
Alien Species ◽  
Invasive Alien Species ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Synthetic Aperture ◽  
Land Use Land Cover ◽  
Alien Plant ◽  
New Generation ◽  
Sentinel 2

The threat of invasive alien plant species is progressively becoming a serious global concern. Alien plant invasions adversely affect both ecological services and socio-economic systems. Hence, accurate detection and mapping of invasive alien species is valuable in mitigating adverse ecological and socio-economic effects. Recent advances in active and passive remote sensing technology have created new and cost-effective opportunities for the application of remote sensing to invasive species mapping. In this study, new generation Sentinel-2 (S2) optical imagery was compared to S2 derived Vegetation Indices (VIs) and S2 VIs fused with Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery for detecting and mapping the American Bramble (Rubuscuneifolius). Fusion of S2 VIs and S1SAR imagery was conducted at pixel level and multi-class Support Vector Machine (SVM) image classification was used to determine the dominant land use land cover classes. Results indicated that S2 derived VIs were the most accurate (80%) in detecting and mapping Bramble, while fused S2 VIs and S1SAR were the least accurate (54%). Findings from this study suggest that the application of S2 VIs is more suitable for Bramble detection and mapping than the fused S2 VIs and S1SAR. The superior performance of S2 VIs highlights the value of the new generation S2 VIs for invasive alien species detection and mapping. Furthermore, this study recommends the use of freely available new generation satellite imagery for cost effective and timeous mapping of Bramble from surrounding native vegetation and other land use land cover types.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2
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